Director Engineering

About the Role

We are looking for a Director of Engineering AI & Full-Stack SaaS to lead the engineering strategy and execution for a highly scalable, enterprise-grade SaaS platform.

This role is ideal for a strong engineering leader who combines deep software engineering expertise, enterprise SaaS experience, cloud-native architecture, full-stack product development, and practical AI/GenAI adoption.

The successful candidate will lead multiple engineering teams, drive architectural and technical decisions, improve engineering velocity and quality, and help embed AI across the software development lifecycle and product engineering ecosystem.

Key Responsibilities

• Lead and mentor multiple engineering teams responsible for building and delivering enterprise SaaS products.
• Define and execute the engineering strategy, technical roadmap, architecture and development standards.
• Drive development of highly scalable, secure, reliable and cloud-native SaaS applications.
• Provide technical leadership across backend, frontend, APIs, microservices and distributed systems.
• Drive adoption of AI/GenAI tools and capabilities across the engineering lifecycle, including development, testing, code quality, productivity and automation.
• Partner closely with Product, Architecture, DevOps, Security and other cross-functional teams to deliver business-critical capabilities.
• Establish engineering best practices around design, coding, testing, CI/CD, observability, performance and reliability.
• Own engineering delivery, quality, scalability and operational excellence across multiple product areas.
• Identify and resolve complex technical and architectural challenges.
• Build a high-performing engineering culture focused on innovation, ownership, collaboration and continuous improvement.
• Evaluate emerging technologies and identify opportunities to leverage AI and automation to improve engineering productivity and product capabilities.
• Drive technical modernization and evolution of existing enterprise platforms.
• Communicate technical strategy, risks, priorities and progress effectively to senior leadership and stakeholders.

Required Skills & Experience

• 15+ years of experience in software/product engineering, with significant experience in engineering leadership roles.
• Proven experience leading large/multi-team engineering organizations.
• Strong background in enterprise SaaS product development.
• Excellent understanding of full-stack software engineering and modern application architecture.
• Strong experience with Java / Spring Boot / or comparable enterprise backend technologies.
• Strong understanding of microservices, APIs, distributed systems and cloud-native architectures.
• Hands-on experience with AWS, Azure or GCP.
• Experience building and scaling highly available, secure and resilient SaaS platforms.
• Strong understanding of modern frontend technologies and full-stack development practices.
• Experience with DevOps, CI/CD, automated testing and engineering productivity practices.
• Demonstrated experience adopting or implementing AI/GenAI technologies within software engineering or enterprise products.
• Strong architectural and technical problem-solving capabilities.
• Excellent people leadership, stakeholder management and communication skills.

Good to Have

• Experience with LLMs, Generative AI, AI agents or AI-powered developer tools.
• Experience integrating AI into enterprise SaaS products.
• Experience driving AI-assisted software development / AI-first SDLC initiatives.
• Experience with workflow automation, orchestration or enterprise automation platforms.
• Experience working with mission-critical enterprise applications.
• Experience managing geographically distributed engineering teams.
• Strong exposure to product-led engineering environments.

Ideal Candidate

The ideal candidate is a technology and people leader, rather than an AI researcher alone. They should have a strong track record of building and scaling enterprise software teams and products, while also demonstrating the ability to identify practical AI opportunities and integrate AI into modern engineering and SaaS product development.

Candidates from enterprise SaaS, cloud platforms, automation, workflow, IT management, enterprise software and AI-enabled product companieswould be particularly relevant.